The dependence of convective core overshooting on stellar mass: reality check, and additional evidence

Overshooting from the convective cores of stars more massive than about 1.2 M(Sun) has a profound impact on their subsequent evolution. And yet, the formulation of the overshooting mechanism in current stellar evolution models has a free parameter (f[ov] in the diffusive approximation) that remains poorly constrained by observations, affecting the determination of astrophysically important quantities such as stellar ages. In an earlier series of papers we assembled a sample of 37 well-measured detached eclipsing binaries to calibrate the dependence of f[ov] on stellar mass, showing that it increases sharply up to a mass of roughly 2 M(Sun), and remains constant thereafter out to at least 4.4 M(Sun). Recent claims have challenged the utility of eclipsing binaries for this purpose, on the basis that the uncertainties in f[ov] from the model fits are typically too large to be useful, casting doubt on a dependence of overshooting on mass. Here we reexamine those claims and show them to be too pessimistic, mainly because they did not account for all available constraints --- both observational and theoretical --- in assessing the true uncertainties. We also take the opportunity to add semi-empirical f[ov] determinations for 13 additional binaries to our previous sample, and to update the values for 9 others. All are consistent with, and strengthen our previous conclusions, supporting a dependence of f[ov] on mass that is now based on estimates for a total of 50 binary systems (100 stars).Comment: 14 pages in emulateapj format, including figures and tables. Accepted for publication in The Astrophysical Journal. One duplicate object has been removed, and the tables and one figure have been update

Cloning Hubble Deep Fields I: A Model-Independent Measurement of Galaxy Evolution

We present a model-independent method of quantifying galaxy evolution in high-resolution images, which we apply to the Hubble Deep Field (HDF). Our procedure is to k-correct all pixels belonging to the images of a complete set of bright galaxies and then to replicate each galaxy image to higher redshift by the product of its space density, 1/V_{max}, and the cosmological volume. The set of bright galaxies is itself selected from the HDF, because presently the HDF provides the highest quality UV images of a redshift-complete sample of galaxies (31 galaxies with I<21.9, \bar{z}=0.5, and for which V/V_{max} is spread fairly). These galaxies are bright enough to permit accurate pixel-by-pixel k-corrections into the restframe UV (\sim 2000 A). We match the shot noise, spatial sampling and PSF smoothing of the HDF data, resulting in entirely empirical and parameter-free ``no-evolution'' deep fields of galaxies for direct comparison with the HDF. In addition, the overcounting rate and the level of incompleteness can be accurately quantified by this procedure. We obtain the following results. Faint HDF galaxies (I>24) are much smaller, more numerous, and less regular than our ``no-evolution'' extrapolation, for any interesting geometry. A higher proportion of HDF galaxies ``dropout'' in both U and B, indicating that some galaxies were brighter at higher redshifts than our ``cloned'' z\sim0.5 population.Comment: 51 pages, 23 figures, replacement includes figures not previously include

Cloning Dropouts: Implications for Galaxy Evolution at High Redshift

The evolution of high redshift galaxies in the two Hubble Deep Fields, HDF-N and HDF-S, is investigated using a cloning technique that replicates z~ 2-3 U dropouts to higher redshifts, allowing a comparison with the observed B and V dropouts at higher redshifts (z ~ 4-5). We treat each galaxy selected for replication as a set of pixels that are k-corrected to higher redshift, accounting for resampling, shot-noise, surface-brightness dimming, and the cosmological model. We find evidence for size evolution (a 1.7x increase) from z ~ 5 to z ~ 2.7 for flat geometries (Omega_M+Omega_LAMBDA=1.0). Simple scaling laws for this cosmology predict that size evolution goes as (1+z)^{-1}, consistent with our result. The UV luminosity density shows a similar increase (1.85x) from z ~ 5 to z ~ 2.7, with minimal evolution in the distribution of intrinsic colors for the dropout population. In general, these results indicate less evolution than was previously reported, and therefore a higher luminosity density at z ~ 4-5 (~ 50% higher) than other estimates. We argue the present technique is the preferred way to understand evolution across samples with differing selection functions, the most relevant differences here being the color cuts and surface brightness thresholds (e.g., due to the (1+z)^4 cosmic surface brightness dimming effect).Comment: 56 pages, 22 figures, accepted for publication in Ap

The Statistical Approach to Quantifying Galaxy Evolution

Studies of the distribution and evolution of galaxies are of fundamental importance to modern cosmology; these studies, however, are hampered by the complexity of the competing effects of spectral and density evolution. Constructing a spectroscopic sample that is able to unambiguously disentangle these processes is currently excessively prohibitive due to the observational requirements. This paper extends and applies an alternative approach that relies on statistical estimates for both distance (z) and spectral type to a deep multi-band dataset that was obtained for this exact purpose. These statistical estimates are extracted directly from the photometric data by capitalizing on the inherent relationships between flux, redshift, and spectral type. These relationships are encapsulated in the empirical photometric redshift relation which we extend to z ~ 1.2, with an intrinsic dispersion of dz = 0.06. We also develop realistic estimates for the photometric redshift error for individual objects, and introduce the utilization of the galaxy ensemble as a tool for quantifying both a cosmological parameter and its measured error. We present deep, multi-band, optical number counts as a demonstration of the integrity of our sample. Using the photometric redshift and the corresponding redshift error, we can divide our data into different redshift intervals and spectral types. As an example application, we present the number redshift distribution as a function of spectral type.Comment: 40 pages (LaTex), 21 Figures, requires aasms4.sty; Accepted by the Astrophysical Journa

An empirical study of evolution of inheritance in Java OSS

Previous studies of Object-Oriented (OO) software have reported avoidance of the inheritance mechanism and cast doubt on the wisdom of ‘deep’ inheritance levels. From an evolutionary perspective, the picture is unclear - we still know relatively little about how, over time, changes tend to be applied by developers. Our conjecture is that an inheritance hierarchy will tend to grow ‘breadth-wise’ rather than ‘depth-wise’. This claim is made on the basis that developers will avoid extending depth in favour of breadth because of the inherent complexity of having to understand the functionality of superclasses. Thus the goal of our study is to investigate this empirically. We conduct an empirical study of seven Java Open-Source Systems (OSSs) over a series of releases to observe the nature and location of changes within the inheritance hierarchies. Results show a strong tendency for classes to be added at levels one and two of the hierarchy (rather than anywhere else). Over 96% of classes added over the course of the versions of all systems were at level 1 or level 2. The results suggest that changes cluster in the shallow levels of a hierarchy; this is relevant for developers since it indicates where remedial activities such as refactoring should be focused

GEMS: Galaxy Evolution from Morphologies and SEDs

GEMS, Galaxy Evolution from Morphologies and SEDs, is a large-area (800 arcmin2) two-color (F606W and F850LP) imaging survey with the Advanced Camera for Surveys on HST. Centered on the Chandra Deep Field South, it covers an area of ~28'x28', or about 120 Hubble Deep Field areas, to a depth of m_AB(F606W)=28.3 (5sigma and m_AB(F850LP)=27.1 (5sigma) for compact sources. In its central ~1/4, GEMS incorporates ACS imaging from the GOODS project. Focusing on the redshift range 0.2<=z<=1.1, GEMS provides morphologies and structural parameters for nearly 10,000 galaxies where redshift estimates, luminosities and SEDs exist from COMBO-17. At the same time, GEMS contains detectable host galaxy images for several hundred faint AGN. This paper provides an overview of the science goals, the experiment design, the data reduction and the science analysis plan for GEMS.Comment: 24 pages, TeX with 6 eps Figures; to appear in ApJ Supplement. Low resolution figures only. Full resolution at http://zwicky.as.arizona.edu/~rix/Misc/GEMS.ps.g

Functional Data Analysis in Electronic Commerce Research

This paper describes opportunities and challenges of using functional data analysis (FDA) for the exploration and analysis of data originating from electronic commerce (eCommerce). We discuss the special data structures that arise in the online environment and why FDA is a natural approach for representing and analyzing such data. The paper reviews several FDA methods and motivates their usefulness in eCommerce research by providing a glimpse into new domain insights that they allow. We argue that the wedding of eCommerce with FDA leads to innovations both in statistical methodology, due to the challenges and complications that arise in eCommerce data, and in online research, by being able to ask (and subsequently answer) new research questions that classical statistical methods are not able to address, and also by expanding on research questions beyond the ones traditionally asked in the offline environment. We describe several applications originating from online transactions which are new to the statistics literature, and point out statistical challenges accompanied by some solutions. We also discuss some promising future directions for joint research efforts between researchers in eCommerce and statistics.Comment: Published at http://dx.doi.org/10.1214/088342306000000132 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org